Intro to Data and Data Science
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What is Analysis? 1.2
‘how’ and ‘why’ something happened performed on past data
What are Analytics? 1.2
Analytics apply logical reasoning to info obtained from analysis Explores the future and looks for patterns 2 types: Qualitative and Quantitative
What are Qualitative Analytics? 1.2
The use of: intuition experience and analysis to plan the next business move
What are Quantitative analytics? 1.2
The application of formulas and algorithms to numbers gathered from analysis
What is Business Intellegence? 1.4
Process of analysing and reporting historical business data Preliminary step to predictive analytics
What is Machine Learning? 1.4
Ability of machines to predict outcomes without being programmed to do so The machines use data to: • Make predictions • analyse patterns • give recommendations
What are advanced analytics? 1.4
all types of analytic processes
Symbolic reasoning is a type of AI that makes an exception and does not use ML and deep learning. It is based on high-level human-readable representations of problems and logic. True or False: Symbolic reasoning is commonly used in practice 1.4
False: Very rarely used in practice.
5 Primary Columns om the 365 infographic 1.5
traditional data big data business intelligence Applying traditional data science techniques Using ML techniques
What is “Data” 2.0
information stored in a digital format used for: a) analysis b) decision making 2 Types: a) Traditional b) Big Data
What is traditional data? 2.0
Data in the form of tables containing numeric or text values; Data that is structured and stored in databases
What is big data? 2.0
Extremely large data; It can be in various formats: • structured • semi-structured • unstructured often characterized by ‘V’ (volume, variety, velocity, etc.)
What is Data Science? 2.0
an interdisciplinary field that combines: statistical, mathematical, programming, problem-solving, and data-management tools.
What are Traditional Methods? 2.0
derived from stats and adapted for business
What is Raw Data? 4.1
AKA Primary Data • cannot be analysed immediately • accumulated and unorganized. The organization is called data collection
What is Class labelling? 4.1
Labelling the data point to the correct data type
What is data cleansing? 4.1
AKA Data Scrubbing • Deals with inconsistent data • -containing typos or missing info
What is data balancing? 4.1
Ensuring the sample gives equal priority to each class
What is Data Shuffling? 4.1
Shuffles data to ensure data is free from unwanted patterns from collection
What is a numerical variable? 4.2
Manipulatable numbers that provide useful information
What is a categorical variable? 4.2
Numbers with no numerical value. Dates are also considered categorical
What is text data mining? 4.3
The process of deriving valuable, unstructured data from text.
What is data masking? 4.3
data masking conceals the original data with random and false data, allows you to conduct analysis and keep confidential information in a secure place.
What is a metric? 4.5
a value derived from obtained measures aims at gauging business performance/progress (has business meaning)